Abstract:

This thesis improves the performance of Kalman Filter and Prony Analysis for tracking multiple ringdown oscillations in stressed transmission networks. Prior to the start of this research, there were no detailed comparisons between both methods. Hence, detailed investigations were first conducted to address the merits of each technique. Subsequently, a number of modifications for enhancing the approximation accuracy of each method have been outlined. Developments were primarily conducted using synthetic signals and, assumed the ambient noise of the applied systems is white. As a result, a sampling scheme has been integrated to traditional Prony Analysis resulting into an Enhanced Prony procedure. Instead of using a fixed sampling interval, the Enhanced Prony Analysis continuously selects a sampling interval appropriate to the network. This was achieved by utilizing a proposed condition number as a quality control index. Unlike the existing relative error approach, the condition number examines the adequacy of the sampling interval without prior knowledge of the values of the modal parameters. Overall, the Enhanced Prony Analysis has been shown to provide more reliable modal estimations. In addition to monitoring the dominant oscillation, the functionality of Kalman Filter was extended to detect multiple modes. By, firstly, redefining the state variables to directly represent the modal contents and, secondly, Hankel Singular Value Decomposition was applied to provide more accurate estimates of the initial state variables. Since network dynamics are not linear, the use of Extended Kalman Filter was also adopted. The improved Kalman Filter is subsequently known as Extended Complex Kalman Filter (ECKF). Unlike Kalman Filter, ECKF is designed to operate in a non-linear non-predetermined operational environment. Its monitoring performance was also verified using a New Zealand case study. Overall, the proposed ECKF technique provided an estimation accuracy at par with Prony Analysis while retaining Kalman's recursive nature of implementation. Although both improved methods are suitable for tracking multiple oscillations simultaneously, ECKF is considered to be the more attractive option for the New Zealand operation. Meanwhile, parallel-processing was applied to both detection methods. Compared with the traditional sequential computation, parallelizing the monitoring algorithms was able to achieve faster computing speeds. Hence, it was identified as a suitable implementation solution for realizing future monitoring algorithms. Lastly, this thesis investigated the operation of Power System Stabilizers (PSS) to damp the inter-area oscillations when utilizing the remote synchrophasor measurements. The objective was to examine the degradation of the damping performance under different load characteristics. Overall, the use of the remote phasor data offered a better observability for the controller. Damping performance was improved when compared with conventional design. PSS, using a combination of remote and local signals, are less affected by the attributes of the load. The worst damping performance was identified under constant power load while the best damping performance occurred under the constant impedance.